{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets\n",
    "\n",
    "All datasets used in the skforecast library and related tutorials are accessible using the `skforecast.datasets.fetch_dataset()` function. \n",
    "Each dataset in this collection comes with a description of its time series and a reference to its original source.\n",
    "\n",
    "Available data sets are stored at [skforecast-datasets](https://github.com/skforecast/skforecast-datasets) and can be easily listed using the `skforecast.datasets.show_datasets_info()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "from skforecast.datasets import fetch_dataset, show_datasets_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the data is structured as a pandas dataframe with a datetime index and frequency. Additionally, a concise description is printed for quick reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────── <span style=\"font-weight: bold\">bike_sharing</span> ──────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 17544 rows x 11 columns                                                  │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────── \u001b[1mbike_sharing\u001b[0m ──────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mShape:\u001b[0m 17544 rows x 11 columns                                                  │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>users</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     holiday  workingday weather  temp   atemp   hum  \\\n",
       "date_time                                                              \n",
       "2011-01-01 00:00:00      0.0         0.0   clear  9.84  14.395  81.0   \n",
       "2011-01-01 01:00:00      0.0         0.0   clear  9.02  13.635  80.0   \n",
       "2011-01-01 02:00:00      0.0         0.0   clear  9.02  13.635  80.0   \n",
       "2011-01-01 03:00:00      0.0         0.0   clear  9.84  14.395  75.0   \n",
       "2011-01-01 04:00:00      0.0         0.0   clear  9.84  14.395  75.0   \n",
       "\n",
       "                     windspeed  users  month  hour  weekday  \n",
       "date_time                                                    \n",
       "2011-01-01 00:00:00        0.0   16.0      1     0        5  \n",
       "2011-01-01 01:00:00        0.0   40.0      1     1        5  \n",
       "2011-01-01 02:00:00        0.0   32.0      1     2        5  \n",
       "2011-01-01 03:00:00        0.0   13.0      1     3        5  \n",
       "2011-01-01 04:00:00        0.0    1.0      1     4        5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data \n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name=\"bike_sharing\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloading raw data, without any preprocessing, is possible by specifying the `raw=True` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────── <span style=\"font-weight: bold\">bike_sharing</span> ──────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 17544 rows x 12 columns                                                  │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────── \u001b[1mbike_sharing\u001b[0m ──────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mShape:\u001b[0m 17544 rows x 12 columns                                                  │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>users</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time  holiday  workingday weather  temp   atemp   hum  \\\n",
       "0  2011-01-01 00:00:00      0.0         0.0   clear  9.84  14.395  81.0   \n",
       "1  2011-01-01 01:00:00      0.0         0.0   clear  9.02  13.635  80.0   \n",
       "2  2011-01-01 02:00:00      0.0         0.0   clear  9.02  13.635  80.0   \n",
       "3  2011-01-01 03:00:00      0.0         0.0   clear  9.84  14.395  75.0   \n",
       "4  2011-01-01 04:00:00      0.0         0.0   clear  9.84  14.395  75.0   \n",
       "\n",
       "   windspeed  users  month  hour  weekday  \n",
       "0        0.0   16.0      1     0        5  \n",
       "1        0.0   40.0      1     1        5  \n",
       "2        0.0   32.0      1     2        5  \n",
       "3        0.0   13.0      1     3        5  \n",
       "4        0.0    1.0      1     4        5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download raw data \n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name=\"bike_sharing\", raw=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────── <span style=\"font-weight: bold\">air_quality_valencia</span> ──────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Hourly measures of several air chemical pollutant at Valencia city (Avd.         │\n",
       "│ Francia) from 2019-01-01 to 20213-12-31. Including the following variables:      │\n",
       "│ pm2.5 (µg/m³), CO (mg/m³), NO (µg/m³), NO2 (µg/m³), PM10 (µg/m³), NOx (µg/m³),   │\n",
       "│ O3 (µg/m³), Veloc. (m/s), Direc. (degrees), SO2 (µg/m³).                         │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Red de Vigilancia y Control de la Contaminación Atmosférica, 46250047-València - │\n",
       "│ Av. França, https://mediambient.gva.es/es/web/calidad-ambiental/datos-           │\n",
       "│ historicos.                                                                      │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/air_quality_valencia.csv                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────── \u001b[1mair_quality_valencia\u001b[0m ──────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Hourly measures of several air chemical pollutant at Valencia city (Avd.         │\n",
       "│ Francia) from 2019-01-01 to 20213-12-31. Including the following variables:      │\n",
       "│ pm2.5 (µg/m³), CO (mg/m³), NO (µg/m³), NO2 (µg/m³), PM10 (µg/m³), NOx (µg/m³),   │\n",
       "│ O3 (µg/m³), Veloc. (m/s), Direc. (degrees), SO2 (µg/m³).                         │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Red de Vigilancia y Control de la Contaminación Atmosférica, 46250047-València - │\n",
       "│ Av. França, https://mediambient.gva.es/es/web/calidad-ambiental/datos-           │\n",
       "│ historicos.                                                                      │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/air_quality_valencia.csv                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────── <span style=\"font-weight: bold\">air_quality_valencia_no_missing</span> ─────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Hourly measures of several air chemical pollutant at Valencia city (Avd.         │\n",
       "│ Francia) from 2019-01-01 to 20213-12-31. Including the following variables:      │\n",
       "│ pm2.5 (µg/m³), CO (mg/m³), NO (µg/m³), NO2 (µg/m³), PM10 (µg/m³), NOx (µg/m³),   │\n",
       "│ O3 (µg/m³), Veloc. (m/s), Direc. (degrees), SO2 (µg/m³). Missing values have     │\n",
       "│ been imputed using linear interpolation.                                         │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Red de Vigilancia y Control de la Contaminación Atmosférica, 46250047-València - │\n",
       "│ Av. França, https://mediambient.gva.es/es/web/calidad-ambiental/datos-           │\n",
       "│ historicos.                                                                      │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/air_quality_valencia_no_missing.csv                           │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────── \u001b[1mair_quality_valencia_no_missing\u001b[0m ─────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Hourly measures of several air chemical pollutant at Valencia city (Avd.         │\n",
       "│ Francia) from 2019-01-01 to 20213-12-31. Including the following variables:      │\n",
       "│ pm2.5 (µg/m³), CO (mg/m³), NO (µg/m³), NO2 (µg/m³), PM10 (µg/m³), NOx (µg/m³),   │\n",
       "│ O3 (µg/m³), Veloc. (m/s), Direc. (degrees), SO2 (µg/m³). Missing values have     │\n",
       "│ been imputed using linear interpolation.                                         │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Red de Vigilancia y Control de la Contaminación Atmosférica, 46250047-València - │\n",
       "│ Av. França, https://mediambient.gva.es/es/web/calidad-ambiental/datos-           │\n",
       "│ historicos.                                                                      │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/air_quality_valencia_no_missing.csv                           │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────────── <span style=\"font-weight: bold\">ashrae_daily</span> ──────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Daily energy consumption data from the ASHRAE competition with building metadata │\n",
       "│ and weather data.                                                                │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Kaggle competition Addison Howard, Chris Balbach, Clayton Miller, Jeff Haberl,   │\n",
       "│ Krishnan Gowri, Sohier Dane. (2019). ASHRAE - Great Energy Predictor III.        │\n",
       "│ Kaggle. https://www.kaggle.com/c/ashrae-energy-prediction/overview               │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://drive.google.com/file/d/1fMsYjfhrFLmeFjKG3jenXjDa5s984ThC/view?usp=shari │\n",
       "│ ng                                                                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────────── \u001b[1mashrae_daily\u001b[0m ──────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Daily energy consumption data from the ASHRAE competition with building metadata │\n",
       "│ and weather data.                                                                │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Kaggle competition Addison Howard, Chris Balbach, Clayton Miller, Jeff Haberl,   │\n",
       "│ Krishnan Gowri, Sohier Dane. (2019). ASHRAE - Great Energy Predictor III.        │\n",
       "│ Kaggle. https://www.kaggle.com/c/ashrae-energy-prediction/overview               │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://drive.google.com/file/d/1fMsYjfhrFLmeFjKG3jenXjDa5s984ThC/view?usp=shari │\n",
       "│ ng                                                                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────── <span style=\"font-weight: bold\">australia_tourism</span> ────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Quarterly overnight trips (in thousands) from 1998 Q1 to 2016 Q4 across          │\n",
       "│ Australia. The tourism regions are formed through the aggregation of Statistical │\n",
       "│ Local Areas (SLAs) which are defined by the various State and Territory tourism  │\n",
       "│ authorities according to their research and marketing needs.                     │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Wang, E, D Cook, and RJ Hyndman (2020). A new tidy data structure to support     │\n",
       "│ exploration and modeling of temporal data, Journal of Computational and          │\n",
       "│ Graphical Statistics, 29:3, 466-478, doi:10.1080/10618600.2019.1695624.          │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/australia_tourism.csv                                         │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────── \u001b[1maustralia_tourism\u001b[0m ────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Quarterly overnight trips (in thousands) from 1998 Q1 to 2016 Q4 across          │\n",
       "│ Australia. The tourism regions are formed through the aggregation of Statistical │\n",
       "│ Local Areas (SLAs) which are defined by the various State and Territory tourism  │\n",
       "│ authorities according to their research and marketing needs.                     │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Wang, E, D Cook, and RJ Hyndman (2020). A new tidy data structure to support     │\n",
       "│ exploration and modeling of temporal data, Journal of Computational and          │\n",
       "│ Graphical Statistics, 29:3, 466-478, doi:10.1080/10618600.2019.1695624.          │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/australia_tourism.csv                                         │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">bdg2_daily</span> ───────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Daily energy consumption data from the The Building Data Genome Project 2 with   │\n",
       "│ building metadata and weather data. https://github.com/buds-lab/building-data-   │\n",
       "│ genome-project-2                                                                 │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome  │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III          │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x  │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://drive.google.com/file/d/1KHYopzclKvS1F6Gt6GoJWKnxiuZ2aqen/view?usp=shari │\n",
       "│ ng                                                                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mbdg2_daily\u001b[0m ───────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Daily energy consumption data from the The Building Data Genome Project 2 with   │\n",
       "│ building metadata and weather data. https://github.com/buds-lab/building-data-   │\n",
       "│ genome-project-2                                                                 │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome  │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III          │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x  │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://drive.google.com/file/d/1KHYopzclKvS1F6Gt6GoJWKnxiuZ2aqen/view?usp=shari │\n",
       "│ ng                                                                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────── <span style=\"font-weight: bold\">bdg2_daily_sample</span> ───────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Daily energy consumption data of two buildings sampled from the The Building    │\n",
       "│ Data Genome Project 2. https://github.com/buds-lab/building-data-genome-        │\n",
       "│ project-2                                                                       │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III         │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/refs/heads/main/data/bdg2_daily_sample.csv                             │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────── \u001b[1mbdg2_daily_sample\u001b[0m ───────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Daily energy consumption data of two buildings sampled from the The Building    │\n",
       "│ Data Genome Project 2. https://github.com/buds-lab/building-data-genome-        │\n",
       "│ project-2                                                                       │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III         │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/refs/heads/main/data/bdg2_daily_sample.csv                             │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────────── <span style=\"font-weight: bold\">bdg2_hourly</span> ───────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Hourly energy consumption data from the The Building Data Genome Project 2 with  │\n",
       "│ building metadata and weather data. https://github.com/buds-lab/building-data-   │\n",
       "│ genome-project-2                                                                 │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome  │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III          │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x  │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://drive.google.com/file/d/1I2i5mZJ82Cl_SHPTaWJmLoaXnntdCgh7/view?usp=shari │\n",
       "│ ng                                                                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────────── \u001b[1mbdg2_hourly\u001b[0m ───────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Hourly energy consumption data from the The Building Data Genome Project 2 with  │\n",
       "│ building metadata and weather data. https://github.com/buds-lab/building-data-   │\n",
       "│ genome-project-2                                                                 │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome  │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III          │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x  │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://drive.google.com/file/d/1I2i5mZJ82Cl_SHPTaWJmLoaXnntdCgh7/view?usp=shari │\n",
       "│ ng                                                                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────── <span style=\"font-weight: bold\">bdg2_hourly_sample</span> ───────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Daily energy consumption data of two buildings sampled from the The Building    │\n",
       "│ Data Genome Project 2. https://github.com/buds-lab/building-data-genome-        │\n",
       "│ project-2                                                                       │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III         │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/refs/heads/main/data/bdg2_hourly_sample.csv                            │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────── \u001b[1mbdg2_hourly_sample\u001b[0m ───────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Daily energy consumption data of two buildings sampled from the The Building    │\n",
       "│ Data Genome Project 2. https://github.com/buds-lab/building-data-genome-        │\n",
       "│ project-2                                                                       │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │\n",
       "│ Project 2, energy meter data from the ASHRAE Great Energy Predictor III         │\n",
       "│ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/refs/heads/main/data/bdg2_hourly_sample.csv                            │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────────── <span style=\"font-weight: bold\">bicimad</span> ─────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ This dataset contains the daily users of the bicycle rental service (BiciMad) in │\n",
       "│ the city of Madrid (Spain) from 2014-06-23 to 2022-09-30.                        │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ The original data was obtained from: Portal de datos abiertos del Ayuntamiento   │\n",
       "│ de Madrid https://datos.madrid.es/portal/site/egob                               │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/bicimad_users.csv                                             │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────────── \u001b[1mbicimad\u001b[0m ─────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ This dataset contains the daily users of the bicycle rental service (BiciMad) in │\n",
       "│ the city of Madrid (Spain) from 2014-06-23 to 2022-09-30.                        │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ The original data was obtained from: Portal de datos abiertos del Ayuntamiento   │\n",
       "│ de Madrid https://datos.madrid.es/portal/site/egob                               │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/bicimad_users.csv                                             │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────── <span style=\"font-weight: bold\">bike_sharing</span> ──────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────── \u001b[1mbike_sharing\u001b[0m ──────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────── <span style=\"font-weight: bold\">bike_sharing_extended_features</span> ─────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, the dataset   │\n",
       "│ was enriched by introducing supplementary features. Addition includes calendar- │\n",
       "│ based variables (day of the week, hour of the day, month, etc.), indicators for │\n",
       "│ sunlight, incorporation of rolling temperature averages, and the creation of    │\n",
       "│ polynomial features generated from variable pairs. All cyclic variables are     │\n",
       "│ encoded using sine and cosine functions to ensure accurate representation.      │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_extended_features.csv                           │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────── \u001b[1mbike_sharing_extended_features\u001b[0m ─────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, the dataset   │\n",
       "│ was enriched by introducing supplementary features. Addition includes calendar- │\n",
       "│ based variables (day of the week, hour of the day, month, etc.), indicators for │\n",
       "│ sunlight, incorporation of rolling temperature averages, and the creation of    │\n",
       "│ polynomial features generated from variable pairs. All cyclic variables are     │\n",
       "│ encoded using sine and cosine functions to ensure accurate representation.      │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_extended_features.csv                           │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────────── <span style=\"font-weight: bold\">ett_m1</span> ─────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL).                                                         │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Zhou, Haoyi &amp; Zhang, Shanghang &amp; Peng, Jieqi &amp; Zhang, Shuai &amp; Li, Jianxin &amp;      │\n",
       "│ Xiong, Hui &amp; Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/ETTm1.csv                                          │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────────── \u001b[1mett_m1\u001b[0m ─────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL).                                                         │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin &      │\n",
       "│ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/ETTm1.csv                                          │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────────── <span style=\"font-weight: bold\">ett_m2</span> ─────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL).                                                         │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Zhou, Haoyi &amp; Zhang, Shanghang &amp; Peng, Jieqi &amp; Zhang, Shuai &amp; Li, Jianxin &amp;      │\n",
       "│ Xiong, Hui &amp; Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2.csv                                                     │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────────── \u001b[1mett_m2\u001b[0m ─────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL).                                                         │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin &      │\n",
       "│ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2.csv                                                     │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">ett_m2_extended</span> ─────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL). Additional variables are created based on calendar      │\n",
       "│ information (year, month, week, day of the week, and hour). These variables have │\n",
       "│ been encoded using the cyclical encoding technique (sin and cos transformations) │\n",
       "│ to preserve the cyclical nature of the data.                                     │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Zhou, Haoyi &amp; Zhang, Shanghang &amp; Peng, Jieqi &amp; Zhang, Shuai &amp; Li, Jianxin &amp;      │\n",
       "│ Xiong, Hui &amp; Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2_extended.csv                                            │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mett_m2_extended\u001b[0m ─────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL). Additional variables are created based on calendar      │\n",
       "│ information (year, month, week, day of the week, and hour). These variables have │\n",
       "│ been encoded using the cyclical encoding technique (sin and cos transformations) │\n",
       "│ to preserve the cyclical nature of the data.                                     │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin &      │\n",
       "│ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2_extended.csv                                            │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────── <span style=\"font-weight: bold\">expenditures_australia</span> ─────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly expenditure on cafes, restaurants and takeaway food services in Victoria │\n",
       "│ (Australia) from April 1982 up to April 2024.                                    │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Australian Bureau of Statistics. Catalogue No. 8501.0                            │\n",
       "│ https://www.abs.gov.au/statistics/industry/retail-and-wholesale-trade/retail-    │\n",
       "│ trade-australia/apr-2024/8501011.xlsx                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/expenditures_australia.csv                         │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────── \u001b[1mexpenditures_australia\u001b[0m ─────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly expenditure on cafes, restaurants and takeaway food services in Victoria │\n",
       "│ (Australia) from April 1982 up to April 2024.                                    │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Australian Bureau of Statistics. Catalogue No. 8501.0                            │\n",
       "│ https://www.abs.gov.au/statistics/industry/retail-and-wholesale-trade/retail-    │\n",
       "│ trade-australia/apr-2024/8501011.xlsx                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/expenditures_australia.csv                         │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">fuel_consumption</span> ────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01.                 │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Obtained from Corporación de Reservas Estratégicas de Productos Petrolíferos and │\n",
       "│ Corporación de Derecho Público tutelada por el Ministerio para la Transición     │\n",
       "│ Ecológica y el Reto Demográfico. https://www.cores.es/es/estadisticas            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/consumos-combustibles-mensual.csv                             │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mfuel_consumption\u001b[0m ────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01.                 │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Obtained from Corporación de Reservas Estratégicas de Productos Petrolíferos and │\n",
       "│ Corporación de Derecho Público tutelada por el Ministerio para la Transición     │\n",
       "│ Ecológica y el Reto Demográfico. https://www.cores.es/es/estadisticas            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/consumos-combustibles-mensual.csv                             │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────────────── <span style=\"font-weight: bold\">h2o</span> ───────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────────────── \u001b[1mh2o\u001b[0m ───────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">h2o_exog</span> ────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mh2o_exog\u001b[0m ────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────── <span style=\"font-weight: bold\">items_sales</span> ───────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                              │\n",
       "│ Simulated time series for the sales of 3 different items. │\n",
       "│                                                           │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                   │\n",
       "│ Simulated data.                                           │\n",
       "│                                                           │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                      │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-  │\n",
       "│ datasets/main/data/simulated_items_sales.csv              │\n",
       "╰───────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────── \u001b[1mitems_sales\u001b[0m ───────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                              │\n",
       "│ Simulated time series for the sales of 3 different items. │\n",
       "│                                                           │\n",
       "│ \u001b[1mSource:\u001b[0m                                                   │\n",
       "│ Simulated data.                                           │\n",
       "│                                                           │\n",
       "│ \u001b[1mURL:\u001b[0m                                                      │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-  │\n",
       "│ datasets/main/data/simulated_items_sales.csv              │\n",
       "╰───────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">m4_daily</span> ────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Time series with daily frequency from the M4 competition.                       │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Monash Time Series Forecasting Repository                                       │\n",
       "│ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb,  │\n",
       "│ G. I., Hyndman, R. J., &amp; Montero-Manso, P. (2021). Monash Time Series           │\n",
       "│ Forecasting Archive. In Neural Information Processing Systems Track on Datasets │\n",
       "│ and Benchmarks.  Raw data, available in .tsf format, has been converted to      │\n",
       "│ Pandas format using the code provided by the authors in                         │\n",
       "│ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py  │\n",
       "│ The category of each time series has been included in the dataset. This         │\n",
       "│ information has been obtained from the Kaggle competition page:                 │\n",
       "│ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset     │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/m4_daily.parquet                                             │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mm4_daily\u001b[0m ────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Time series with daily frequency from the M4 competition.                       │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Monash Time Series Forecasting Repository                                       │\n",
       "│ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb,  │\n",
       "│ G. I., Hyndman, R. J., & Montero-Manso, P. (2021). Monash Time Series           │\n",
       "│ Forecasting Archive. In Neural Information Processing Systems Track on Datasets │\n",
       "│ and Benchmarks.  Raw data, available in .tsf format, has been converted to      │\n",
       "│ Pandas format using the code provided by the authors in                         │\n",
       "│ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py  │\n",
       "│ The category of each time series has been included in the dataset. This         │\n",
       "│ information has been obtained from the Kaggle competition page:                 │\n",
       "│ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset     │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/m4_daily.parquet                                             │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">m4_hourly</span> ───────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Time series with hourly frequency from the M4 competition.                      │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Monash Time Series Forecasting Repository                                       │\n",
       "│ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb,  │\n",
       "│ G. I., Hyndman, R. J., &amp; Montero-Manso, P. (2021). Monash Time Series           │\n",
       "│ Forecasting Archive. In Neural Information Processing Systems Track on Datasets │\n",
       "│ and Benchmarks.  Raw data, available in .tsf format, has been converted to      │\n",
       "│ Pandas format using the code provided by the authors in                         │\n",
       "│ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py  │\n",
       "│ The category of each time series has been included in the dataset. This         │\n",
       "│ information has been obtained from the Kaggle competition page:                 │\n",
       "│ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset     │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/m4_hourly.parquet                                            │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mm4_hourly\u001b[0m ───────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Time series with hourly frequency from the M4 competition.                      │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Monash Time Series Forecasting Repository                                       │\n",
       "│ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb,  │\n",
       "│ G. I., Hyndman, R. J., & Montero-Manso, P. (2021). Monash Time Series           │\n",
       "│ Forecasting Archive. In Neural Information Processing Systems Track on Datasets │\n",
       "│ and Benchmarks.  Raw data, available in .tsf format, has been converted to      │\n",
       "│ Pandas format using the code provided by the authors in                         │\n",
       "│ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py  │\n",
       "│ The category of each time series has been included in the dataset. This         │\n",
       "│ information has been obtained from the Kaggle competition page:                 │\n",
       "│ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset     │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/m4_hourly.parquet                                            │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────────── <span style=\"font-weight: bold\">m5</span> ───────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Daily sales data from the M5 competition with product metadata and calendar      │\n",
       "│ data.                                                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Addison Howard, inversion, Spyros Makridakis, and vangelis. M5 Forecasting -     │\n",
       "│ Accuracy. https://kaggle.com/competitions/m5-forecasting-accuracy, 2020. Kaggle. │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ Data is stored in multiple files:   https://drive.google.com/file/d/1JOqBsSHegly │\n",
       "│ 6iSJFgmkugAko734c6ZW5/view?usp=sharing   https://drive.google.com/file/d/1BhO1BU │\n",
       "│ vs-d7ipXrm7caC3Wd_d0C_6PZ8/view?usp=sharing   https://drive.google.com/file/d/1o │\n",
       "│ HwkQ_QycJVTZMb6bH8C2klQB971gXXA/view?usp=sharing   https://drive.google.com/file │\n",
       "│ /d/1OvYzFlDG04YgTvju2k02vHEOj0nIuwei/view?usp=sharing                            │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────────── \u001b[1mm5\u001b[0m ───────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Daily sales data from the M5 competition with product metadata and calendar      │\n",
       "│ data.                                                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Addison Howard, inversion, Spyros Makridakis, and vangelis. M5 Forecasting -     │\n",
       "│ Accuracy. https://kaggle.com/competitions/m5-forecasting-accuracy, 2020. Kaggle. │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ Data is stored in multiple files:   https://drive.google.com/file/d/1JOqBsSHegly │\n",
       "│ 6iSJFgmkugAko734c6ZW5/view?usp=sharing   https://drive.google.com/file/d/1BhO1BU │\n",
       "│ vs-d7ipXrm7caC3Wd_d0C_6PZ8/view?usp=sharing   https://drive.google.com/file/d/1o │\n",
       "│ HwkQ_QycJVTZMb6bH8C2klQB971gXXA/view?usp=sharing   https://drive.google.com/file │\n",
       "│ /d/1OvYzFlDG04YgTvju2k02vHEOj0nIuwei/view?usp=sharing                            │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────── <span style=\"font-weight: bold\">public_transport_madrid</span> ─────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Daily users of public transport in Madrid (Spain) from 2023-01-01 to 2024-12-15. │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Consorcio Regional de Transportes de Madrid CRTM, CRTM Evolucion demanda diaria  │\n",
       "│ https://datos.crtm.es/documents/a7210254c4514a19a51b1617cfd61f75/about           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/public-transport-madrid.csv                        │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────── \u001b[1mpublic_transport_madrid\u001b[0m ─────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Daily users of public transport in Madrid (Spain) from 2023-01-01 to 2024-12-15. │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Consorcio Regional de Transportes de Madrid CRTM, CRTM Evolucion demanda diaria  │\n",
       "│ https://datos.crtm.es/documents/a7210254c4514a19a51b1617cfd61f75/about           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/public-transport-madrid.csv                        │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────── <span style=\"font-weight: bold\">store_sales</span> ──────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                   │\n",
       "│ This dataset contains 913,000 sales transactions from 2013-01-01 to 2017-12-31 │\n",
       "│ for 50 products (SKU) in 10 stores.                                            │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                        │\n",
       "│ The original data was obtained from: inversion. (2018). Store Item Demand      │\n",
       "│ Forecasting Challenge. Kaggle. https://kaggle.com/competitions/demand-         │\n",
       "│ forecasting-kernels-only                                                       │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                           │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                       │\n",
       "│ datasets/main/data/store_sales.csv                                             │\n",
       "╰────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────── \u001b[1mstore_sales\u001b[0m ──────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                   │\n",
       "│ This dataset contains 913,000 sales transactions from 2013-01-01 to 2017-12-31 │\n",
       "│ for 50 products (SKU) in 10 stores.                                            │\n",
       "│                                                                                │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                        │\n",
       "│ The original data was obtained from: inversion. (2018). Store Item Demand      │\n",
       "│ Forecasting Challenge. Kaggle. https://kaggle.com/competitions/demand-         │\n",
       "│ forecasting-kernels-only                                                       │\n",
       "│                                                                                │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                           │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                       │\n",
       "│ datasets/main/data/store_sales.csv                                             │\n",
       "╰────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">turbine_emission</span> ────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ The dataset contains 36733 instances of 11 sensor measures aggregated over one   │\n",
       "│ hour, from a gas turbine located in Turkey for the purpose of studying flue gas  │\n",
       "│ emissions, namely CO and NOx. Available variables include: Ambient temperature   │\n",
       "│ (AT), Ambient pressure (AP), Ambient humidity (AH), Air filter difference        │\n",
       "│ pressure (AFDP), Gas turbine exhaust pressure (GTEP), Turbine inlet temperature  │\n",
       "│ (TIT), Turbine after temperature (TAT), Compressor discharge pressure (CDP),     │\n",
       "│ Turbine energy yield (TEY), Carbon monoxide (CO), and Nitrogen oxides (NOx).     │\n",
       "│ Covered period from 2011-01-01 00:00:00 to 2015-03-11 12:00:00.                  │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ https://archive.ics.uci.edu/dataset/551/gas+turbine+co+and+nox+emission+data+set │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/turbine_emission.csv                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mturbine_emission\u001b[0m ────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ The dataset contains 36733 instances of 11 sensor measures aggregated over one   │\n",
       "│ hour, from a gas turbine located in Turkey for the purpose of studying flue gas  │\n",
       "│ emissions, namely CO and NOx. Available variables include: Ambient temperature   │\n",
       "│ (AT), Ambient pressure (AP), Ambient humidity (AH), Air filter difference        │\n",
       "│ pressure (AFDP), Gas turbine exhaust pressure (GTEP), Turbine inlet temperature  │\n",
       "│ (TIT), Turbine after temperature (TAT), Compressor discharge pressure (CDP),     │\n",
       "│ Turbine energy yield (TEY), Carbon monoxide (CO), and Nitrogen oxides (NOx).     │\n",
       "│ Covered period from 2011-01-01 00:00:00 to 2015-03-11 12:00:00.                  │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ https://archive.ics.uci.edu/dataset/551/gas+turbine+co+and+nox+emission+data+set │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/refs/heads/main/data/turbine_emission.csv                               │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">uk_daily_flights</span> ────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Daily number of flights in UK from 02/01/2019 to 23/01/2022.                     │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Experimental statistics published as part of the Economic activity and social    │\n",
       "│ change in the UK, real-time indicators release, Published 27 January 2022. Daily │\n",
       "│ flight numbers are available in the dashboard provided by the European           │\n",
       "│ Organisation for the Safety of Air Navigation (EUROCONTROL). https://www.ons.gov │\n",
       "│ .uk/economy/economicoutputandproductivity/output/bulletins/economicactivityandso │\n",
       "│ cialchangeintheukrealtimeindicators/latest                                       │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/uk_daily_flights.csv                                          │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1muk_daily_flights\u001b[0m ────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Daily number of flights in UK from 02/01/2019 to 23/01/2022.                     │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Experimental statistics published as part of the Economic activity and social    │\n",
       "│ change in the UK, real-time indicators release, Published 27 January 2022. Daily │\n",
       "│ flight numbers are available in the dashboard provided by the European           │\n",
       "│ Organisation for the Safety of Air Navigation (EUROCONTROL). https://www.ons.gov │\n",
       "│ .uk/economy/economicoutputandproductivity/output/bulletins/economicactivityandso │\n",
       "│ cialchangeintheukrealtimeindicators/latest                                       │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/uk_daily_flights.csv                                          │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────── <span style=\"font-weight: bold\">vic_electricity</span> ─────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                             │\n",
       "│ Half-hourly electricity demand for Victoria, Australia                   │\n",
       "│                                                                          │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                  │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,              │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                               │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                │\n",
       "│                                                                          │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                     │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                 │\n",
       "│ datasets/main/data/vic_electricity.csv                                   │\n",
       "╰──────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────── \u001b[1mvic_electricity\u001b[0m ─────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                             │\n",
       "│ Half-hourly electricity demand for Victoria, Australia                   │\n",
       "│                                                                          │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                  │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,              │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                               │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                │\n",
       "│                                                                          │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                     │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                 │\n",
       "│ datasets/main/data/vic_electricity.csv                                   │\n",
       "╰──────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────── <span style=\"font-weight: bold\">vic_electricity_classification</span> ────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                   │\n",
       "│ Hourly electricity demand for Victoria, Australia classified into three        │\n",
       "│ categories: 'low', 'medium' and 'high' according to the 20th and 80th          │\n",
       "│ percentiles. The dataset also includes temperature, holiday indicator and hour │\n",
       "│ of the day as features.                                                        │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                        │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse       │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,                    │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                                     │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                      │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                           │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                       │\n",
       "│ datasets/main/data/vic_electricity_classification.csv                          │\n",
       "╰────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────── \u001b[1mvic_electricity_classification\u001b[0m ────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                   │\n",
       "│ Hourly electricity demand for Victoria, Australia classified into three        │\n",
       "│ categories: 'low', 'medium' and 'high' according to the 20th and 80th          │\n",
       "│ percentiles. The dataset also includes temperature, holiday indicator and hour │\n",
       "│ of the day as features.                                                        │\n",
       "│                                                                                │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                        │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse       │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,                    │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                                     │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                      │\n",
       "│                                                                                │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                           │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                       │\n",
       "│ datasets/main/data/vic_electricity_classification.csv                          │\n",
       "╰────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────── <span style=\"font-weight: bold\">website_visits</span> ──────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                              │\n",
       "│ Daily visits to the cienciadedatos.net website registered with the google │\n",
       "│ analytics service.                                                        │\n",
       "│                                                                           │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                   │\n",
       "│ Amat Rodrigo, J. (2021). cienciadedatos.net (1.0.0). Zenodo.              │\n",
       "│ https://doi.org/10.5281/zenodo.10006330                                   │\n",
       "│                                                                           │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                      │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                  │\n",
       "│ datasets/main/data/visitas_por_dia_web_cienciadedatos.csv                 │\n",
       "╰───────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────── \u001b[1mwebsite_visits\u001b[0m ──────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                              │\n",
       "│ Daily visits to the cienciadedatos.net website registered with the google │\n",
       "│ analytics service.                                                        │\n",
       "│                                                                           │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                   │\n",
       "│ Amat Rodrigo, J. (2021). cienciadedatos.net (1.0.0). Zenodo.              │\n",
       "│ https://doi.org/10.5281/zenodo.10006330                                   │\n",
       "│                                                                           │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                      │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                  │\n",
       "│ datasets/main/data/visitas_por_dia_web_cienciadedatos.csv                 │\n",
       "╰───────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">wikipedia_visits</span> ────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Log daily page views for the Wikipedia page for Peyton Manning. Scraped data     │\n",
       "│ using the Wikipediatrend package in R.                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ https://github.com/facebook/prophet/blob/{version}/examples/example_wp_log_peyto │\n",
       "│ n_manning.csv                                                                    │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/wikipedia_visits.csv                                          │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mwikipedia_visits\u001b[0m ────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Log daily page views for the Wikipedia page for Peyton Manning. Scraped data     │\n",
       "│ using the Wikipediatrend package in R.                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ https://github.com/facebook/prophet/blob/{version}/examples/example_wp_log_peyto │\n",
       "│ n_manning.csv                                                                    │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/wikipedia_visits.csv                                          │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show dataset information\n",
    "# ==============================================================================\n",
    "show_datasets_info(datasets_names=None)  # None means all datasets"
   ]
  }
 ],
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